It’s a real shame Dijkstra rubbed so many people the wrong way.
Maybe his incisive polemic, which I greatly enjoy, was all but pandering to a certain elitist sensibility in the end.
To make manageable programs, you have to trade off execution speed both on the cpu and in the organization. His rather mathematized prescriptions imply we should hire quarrelsome academics such as him to reduce performance and slow down product development[initially…] all in the interest of his stratified sensibilities of elegance and simplicity.
These are all expository diagrams. Second-hand and auxiliary by nature.
Diagrams can be authoritative, and the ones I’ve seen will break some or all of these rules because they represent natural heuristics that practitioners are expected to fill inn themselves.
> In the aggregate, almost no programmer can think up code faster than they can type it in.
And thank god! Code is a liability. The price of code is coming down but selling code is almost entirely supplanted by selling features (SaaS) as a business model. The early cloud services have become legacy dependencies by now (great work if you can get it). Maintaining code is becoming a central business concern in all sectors governed by IT (i.e. all sectors, eating the world and all that).
On a per-feature basis, more code means higher maintenance costs, more bugs and greater demands on developer skills and experience. Validated production code that delivers proven customer value is not something you refactor on a whim (unless you plan to go out of business), and the fact that you did it in an evening thanks to ClippyGPT means nothing—-the costly part is always what comes after: demonstrating value or maintaing trust in a competitive market with a much shallower capital investment moat.
I find the prevailing use of “recursion”, i.e. β-reduction all the way to ground terms, to be an impoverished sense of the term.
By all means, you should be familiar with the evaluation semantics of your runtime environment. If you don’t know the search depth of your input or can’t otherwise constrain stack height beforehand beware the specter of symbolic recursion, but recursion is so much more.
Functional reactive programs do not suffer from stack-overflow on recursion (implementation details notwithstanding). By Church-Turing every sombolic-recursive function can be translated to a looped iteration over some memoization of intermeduate results. The stack is just a special case of such memoization. Popular functional programming patterns provide other bottled up memoization strategies: Fold/Reduce, map/zip/join, either/amb/cond the list goes on (heh). Your iterating loop is still traversing the solution space in a recursive manner, provided you dot your ‘i’s and carry your remainders correctly.
Heck, any feedback circuit is essentially recursive and I have never seen an IIR-filter blow the stack (by itself, mind you).
Most vendors use three indexes for triples and 4 or 6 for quads. All the indexes are covering, which is to say they triplicate all data—-in other words the database consists only of indexes.
With the caveat that I was exactly wrong about the books de-listing, I feel you are making my point for me and retreating to a more pragmatic position about defaults.
The (quite entertaining) saga of Nightshade tells a story about what is going to be content creators “default position” going forward and everyone else will follow. You would be a fool not to, the AI companies are trying to end run you, using your own content, and make a profit without compensating you and leave you with no recourse.
Listen, most website and book-authors want to be indexed by google. It brings potential audience their way, so most don’t make use of their _right_ to be de-listed. For these models, there is no plausible benefit to the original creators, and so one has to argue they have _no_ such right to be “de-listed” in order to get any training data currently under copyright.
Well then I could have been much clearer because I meant something like the latter.
An ML model can neither have nor be in breach of copyright so any discussion about how it works, and how that relates to how people work or “learn” is besides the point.
What actually matters is firstly details about collation of source material, and later the particular legal details surrounding attribution. The last part involves breaking new ground legally speaking and IANAL so I will reserve judgement. The first part, collation of source material for training is emphatically not unexplored legal or moral territory. People are acting like none of the established processes apply in the case of LLMs and handwave about “learning” to defend it.